Caching can encounter challenges that include, for example, the problem of cache warm-up, where cache needs to be loaded with enough active data to reduce cache misses and allow it to start improving I/O response times.
There are also problems associated with caching software, most notably issues of data integrity and cache coherence in clustered deployments.
In response, some suppliers have chosen to implement only write-through cache, whereas others have resolved the coherence issue by replicating data between cluster nodes.
There will always be a trade-off between latency and resiliency and so it becomes dependent on the user to look at whether write-cache is an essential requirement of the deployment.
One other consideration is the algorithms or logic used to determine what to cache. Some solutions use simple “least recently used” policies to discard data; others are more complex and look at the data for clues as to which should be retained in cache.
Users should look at the cache occupancy criteria and ensure the rules fit the types of data they expect the cache to manage.
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